Data Visualisation with Hadoop Mashups, Hive, Power BI and Excel 2013Jen Stirrup
This session focused on data visualisation using Power BI, based on big data. Some examples of Hive and HDFS file storage are given. An overview of Microsoft HDInsight is supplied.
Sql rally amsterdam Aanalysing data with Power BI and HiveJen Stirrup
Analyzing Data with Power View (Level 100)
Jen Stirrup
Come learn about the best ways to present data to your Business Intelligence data consumers, and see how to apply these principles in Power View, Microsoft's data visualization tool. Using demos, we will investigate Power View based on current cognitive research around data visualization principles from such experts as Stephen Few, Edware Tufte, and others. We will then examine how data can be analyzed with Power View and look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack.
Data Visualisation with Hadoop Mashups, Hive, Power BI and Excel 2013Jen Stirrup
This session focused on data visualisation using Power BI, based on big data. Some examples of Hive and HDFS file storage are given. An overview of Microsoft HDInsight is supplied.
Sql rally amsterdam Aanalysing data with Power BI and HiveJen Stirrup
Analyzing Data with Power View (Level 100)
Jen Stirrup
Come learn about the best ways to present data to your Business Intelligence data consumers, and see how to apply these principles in Power View, Microsoft's data visualization tool. Using demos, we will investigate Power View based on current cognitive research around data visualization principles from such experts as Stephen Few, Edware Tufte, and others. We will then examine how data can be analyzed with Power View and look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack.
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
Want to learn Hadoop online? This PPT give you Introduction to Big Data Hadoop Training Online by expert trainers at ITJobZone.biz - Start your Hadoop Online training with this Presentation.
"Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.” Howard Aiken, Founder of Harvard’s Computing Science Program.
Data is moving so fast these days, and there is a shift whereby people are paying for value, not technology. This is where cloud computing comes in: it is very empowering, because anyone with an internet connection can access it. With Power BI in the cloud, small businesses are liberated with the ability to use the same tools and techniques to explore ideas as larger organisations.
In this session, we will look at understanding the Power BI components and tools available in the cloud, including the Power BI Admin Center, Power Query, Power Pivot, Power View and Power Map. We will look at how to use them will accelerate ideas and help to clarify decisions, and related to this, discuss the roles within IT and the business in relation to these tools. We will also look at business puzzles versus business mysteries, a definition evoked by Malcolm Gladwell (Blink, Outliers) in relation to Power BI.
“Out there in some garage is an entrepreneur who’s forging a bullet with your company’s name on it,” said Gary Hamel, a management guru. With Power BI, let’s see how you can translate your ideas in to a message that people can see, using cloud as an empowerment tool.
Introduction To Big Data Analytics On Hadoop - SpringPeopleSpringPeople
48 hours of video are uploaded to YouTube every minute, resulting in nearly 8 years of content every day.
This is where comes the role of Big Data analytics so that huge amount of data can be maintained easily.
A brief introduction to Big Data Analytics On Hadoop.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
Want to learn Hadoop online? This PPT give you Introduction to Big Data Hadoop Training Online by expert trainers at ITJobZone.biz - Start your Hadoop Online training with this Presentation.
"Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.” Howard Aiken, Founder of Harvard’s Computing Science Program.
Data is moving so fast these days, and there is a shift whereby people are paying for value, not technology. This is where cloud computing comes in: it is very empowering, because anyone with an internet connection can access it. With Power BI in the cloud, small businesses are liberated with the ability to use the same tools and techniques to explore ideas as larger organisations.
In this session, we will look at understanding the Power BI components and tools available in the cloud, including the Power BI Admin Center, Power Query, Power Pivot, Power View and Power Map. We will look at how to use them will accelerate ideas and help to clarify decisions, and related to this, discuss the roles within IT and the business in relation to these tools. We will also look at business puzzles versus business mysteries, a definition evoked by Malcolm Gladwell (Blink, Outliers) in relation to Power BI.
“Out there in some garage is an entrepreneur who’s forging a bullet with your company’s name on it,” said Gary Hamel, a management guru. With Power BI, let’s see how you can translate your ideas in to a message that people can see, using cloud as an empowerment tool.
Introduction To Big Data Analytics On Hadoop - SpringPeopleSpringPeople
48 hours of video are uploaded to YouTube every minute, resulting in nearly 8 years of content every day.
This is where comes the role of Big Data analytics so that huge amount of data can be maintained easily.
A brief introduction to Big Data Analytics On Hadoop.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Slides used for the keynote at the even Big Data & Data Science http://eventos.citius.usc.es/bigdata/
Some slides are borrowed from random hadoop/big data presentations
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
Automate your Data Science pipeline with Ansible, Python and Kubernetes - ODSC Talk
What is Data Science and the Data Science Landscape
Process and Flow
Understanding Data
The Data Science Toolkit
The Big Data Challenge
Cloud Computing Solutions
The rise of DevOps in Data Science
Automate your data pipeline with Ansible
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
La BuzzWord dell’ultimo anno è “Data Science”. Ma cosa significa realmente? Cosa fa un “Data Scientist”? Che strumenti sono messi a disposizione da Microsoft? E che altri strumenti ci sono oltre a Microsoft?
Big Data brings big promise and also big challenges, the primary and most important one being the ability to deliver Value to business stakeholders who are not data scientists!
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
AI Applications in Healthcare and Medicine.pdfJen Stirrup
This session was delivered for the Global Business Roundtable. The topic: AI applications in Healthcare and Medicine. In this session, Jennifer Stirrup takes people through a general process of adopting AI in their organisations.
BUILDING A STRONG FOUNDATION FOR SUCCESS WITH BI AND DIGITAL TRANSFORMATIONJen Stirrup
The objective of Digital Transformation is improve the quality and resilience of digital services to serve customers better, and data is a cruel part of fulfilling that ambition. As the organisation moves forward in pursuit of its strategic ambitions, it will need to remain focused on the stabilisation and improvement of existing technology and data foundations. To succeed, organisations need continuously strive to improve data, systems and processes for people using digital solutions; it is not simply digitising paper processes. The challenge of digital transformation is to work with people, but how can you build systems that serve them well to achieve and deliver more in a customer-focused way? Innovators will relish the opportunity to adopt new technology, but laggars are often waiting for proof that this will help them deliver better services or products. The challenge is that the adoption of digital solutions varies significantly from one person to the next, one team to the next and one organisation to the next. In this keynote, there will be a discussion of the industry landscape followed by takeaways that will help digital transformation in your organization.
1. Do more than get the basics right
2. Build confidence in changes through better use of data
3. How to oversee delivery while considering strategy
CuRious about R in Power BI? End to end R in Power BI for beginners Jen Stirrup
In this session, we will start R right from the beginning, from installing R through to datatransformation and integration, through to visualizing data by using R in PowerBI. Then, we will move towards powerful but simple to use datatypes in R such as data frames. We will also upgrade our data analysis skills by looking at Rdata transformation using a powerful set of tools to make things simple: the tidyverse. Then, we will look at integrating our R work into Power BI, and visualizing our data using beautiful visualizations with R and Power BI. Finally, we will share our work by publishing our Power BI project, with our R code, to the Power BI service. We will also look at refreshing our dataset so that our new dashboard has refreshed data.
This session is aimed at getting beginners up to speed as gently and quickly as possible. Join this session if you are curious about R and want to know more. If you are already a Power BI expert, join this session to open up a whole new world of Power BI to add toyour skill set. If you are new to Power BI, you will still get value from this session since you'll be able to see a Power BI dashboard being built in an end-to-end solution.
Artificial Intelligence Ethics keynote: With Great Power, comes Great Respons...Jen Stirrup
Artificial Intelligence has been receiving some bad press recently, with respect to its ethical consequences in terms of changes to working conditions, deepfake technology and even job losses. Organizations are concerned about bias in their data, perpetuating stereotypes and neglecting responsibility. How can AI systems treat all people fairly? What about concerns of safety and reliability?
In this keynote, we will explore the toolkits available in Azure to help businesses to navigate the complex ethics environment. Join this session to understand what Microsoft can offer in terms of supporting organisations to consider ethics as an integral part of their AI solutions.
1 Introduction to Microsoft data platform analytics for releaseJen Stirrup
Part 1 of a conference workshop. This forms the morning session, which looks at moving from Business Intelligence to Analytics.
Topics Covered: Azure Data Explorer, Azure Data Factory, Azure Synapse Analytics, Event Hubs, HDInsight, Big Data
Comparing Microsoft Big Data Platform TechnologiesJen Stirrup
In this segment, we look at technologies such as HDInsight, Azure Databricks, Azure Data Lake Analytics and Apache Spark. We compare the technologies to help you to decide the best technology for your situation.
Introduction to Analytics with Azure Notebooks and PythonJen Stirrup
Introduction to Analytics with Azure Notebooks and Python for Data Science and Business Intelligence. This is one part of a full day workshop on moving from BI to Analytics
When looking at Sales Analytics, where should you start? What should you measure? This session provides ideas on sales metrics, implemented in Power BI
Diversity and inclusion for the newbies and doersJen Stirrup
This presentation is aimed at people who want to *do* something positive for diversity and inclusion in their workplaces and communities, but don't know where to start to have a quick impact. I've made up a checklist of 7 'E's to help people along. We cover crucial topics such as: • What can we do to tackle unconscious bias in our systems, solutions and interactions with others? • How can we be more inclusive towards others? • How can we encourage and mentor younger generations to get involved in STEM topics and technical roles both as leaders and in the communities of people who surround us? I hope you enjoy this interactive and thought-provoking discussion of diversity and inclusion, aimed at people who want to get started and do something positive and impactful to help others.
Artificial Intelligence from the Business perspectiveJen Stirrup
What is AI from the Business perspective? In this presentation, Jen Stirrup discusses the 8 'C's of Artificial Intelligence from the business leadership perspective.
How to be successful with Artificial Intelligence - from small to successJen Stirrup
Keynote from AI World Congress in October 2019. Artificial Intelligence isn't just for the technies; it is crucial that business-oriented individuals adopt this technology, which can be conceived as the fourth industrial age. Artificial intelligence is becoming closer to being a a part of our daily lives through the use of technologies like virtual assistants such as Alexa, smart homes, and automated customer service. Now, we are running the race not just to win, but to survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and futurist ideas are developing into reality at accelerated rates.
How can you help your your company to evolve, adapt and succeed using Artificial Intelligence to stay at the forefront of the competition, and win the race for AI adoption in your organization? What are the potential issues, complications and benefits that artificial intelligence could bring to us and our organisations? In this session, Jen Stirrup will explain the quick wins to win the Red Queen's Race in Artificial Intelligence.
Artificial Intelligence: Winning the Red Queen’s Race Keynote at ESPC with Je...Jen Stirrup
Artificial Intelligence is popularised in fiction films such as “The Terminator” and “AI: Artificial Intelligence”. Now, artificial intelligence is becoming closer to being a part of our daily lives through the use of technologies like virtual assistants such as Cortana, smart homes, and automated customer service.
Now, we are running the Red Queen’s race not just to win, but to survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and futurist ideas are developing into reality at accelerated rates.
How can you help your your company to evolve, adapt and succeed using Artificial Intelligence to stay at the forefront of the competition, and win the Red Queen’s Race? What are the potential issues, complications and benefits that artificial intelligence could bring to us and our organisations?
In this keynote, Jen Stirrup explains the quick wins to win the Red Queen’s Race, using demos from Microsoft technologies such as AutoML to help you and your organisation win the Red Queen’s race.
Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!
R - what do the numbers mean? #RStats This is the presentation for my Demo at Orlando Live60 AILIve. We go through statistics interpretation with examples
Artificial Intelligence and Deep Learning in Azure, CNTK and TensorflowJen Stirrup
Artificial Intelligence and Deep Learning in Azure, using Open Source technologies CNTK and Tensorflow. The tutorial can be found on GitHub here: https://github.com/Microsoft/CNTK/tree/master/Tutorials
and the CNTK video can be found here: https://youtu.be/qgwaP43ZIwA
Blockchain Demystified for Business Intelligence ProfessionalsJen Stirrup
Blockchain is a transformational technology with the potential to extend digital transformation beyond an organization and into the processes it shares with suppliers, customers, and partners.
What is blockchain? What can it do for my organization? How can your organisation manage a blockchain implementation? How does it work in Azure?
Join this session to learn about blockchain and see it in action. We will also discuss the use cases for blockchain, and whether it is here to stay.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
6. As long as you’re gonna be thinking anyway,
why not think big. (Donald Trump)
Because we can imagine, we are free (Jean-
Paul Satre)
What kind of modern world would we have if
Edison, Green and Dixon had not developed
cinematic technology before Hitchcock grew
up? (Kevin Kelly, futurist)
7. The Unknown Unknowns
• That is to say, there are things that we know
we don't know. But there are also unknown
unknowns. There are things we don't know
we don't know. (Donald Rumsfeld)
21. Increases ad revenue by processing 3.5
billion events per day
Massive Volumes
Processes 464 billion rows per quarter,
with average query time under 10 secs.
Measures and ranks online user
influence by processing 3 billion signals
per day
Cloud Connectivity
Connects across 15 social networks via
the cloud for data and API access
Uses sentiment analysis and web
analytics for its internal cloud
Real-Time Insight
Improves operational decision making
for IT managers and users
24. What is Hadoop?
“Flexible and Available
Architecture for Large Scale
computation and data processing
on a network of highly available
commodity hardware.”
37. BIG DATA REQUIRES AN END-TO-END APPROACH
Discover Combine Refine
Relational Non-relational Streaming
INSIGHT
DATA
ENRICHMENT
DATA
MANAGEMENT
Self-Service Collaboration Corporate Apps Devices
Analytical
39. Microsoft Hadoop Vision
Runs on Windows and Azure
• Active Directory
• System Center
• .Net Programmability
Microsoft Data Connectivity
• SQL Server / SQL Parallel Data Warehouse
• Azure Storage / Azure Data Market
40. Microsoft Hadoop Vision
Microsoft Business Intelligence
• Hive ODBC Connectivity
• BI Tools for Big Data
Collaboratewith and Contribute to OSS
• Collaborate with HortonWorks
• Provide improvements and Windows support back to OSS
41. On Premise
• Comes with:
•Hadoop command line (shell)
•Hadoop Status for name node and
map-reduce cluster
•HDInsight Dashboard
42. On Premise
• On prem:
http://www.microsoft.com/bigda
ta/
• Single node cluster (onebox) install
• C:hadoop
• Starts local services
43. On Azure
• On Windows Azure:
http://HadoopOnAzure.com/
• 3 node cluster running as a service in Azure
• Can be used for 5 days
• Provides samples and HDInsight Dashboard
• TAP Program
44. Agenda
•Big Data – What is it?
• Big Data or Big Hype?
• Big Data, Big Insights with
Hadoop
45. Because we can imagine,
we are free
Jean-Paul Satre
We have the tools. All we’ve got to
do is imagine what could be. We can
reinvent the present; we can
transform the world around us.
Jason Silva
Relational databases are pushed to the limit.Data Management techniques haven't scaledTraditional systems haven't scaledBig data is about complexity as well as scalability.NoSQL as a paradigm shift.Hadoop can run and parallelise large scale batch computations on large amounts of data. however, there is a high latency in returning the results. It is not suitable for low latency.What are the features of a Big Data system?RobustFault TolerantHuman Fault TolerantData when you need itScaleableGeneralExtensibleReduced implementation complexityError handlingAuditing-- no different from a little Data Solution. Think inserts.
Relational databases are pushed to the limit.Data Management techniques haven't scaledTraditional systems haven't scaledBig data is about complexity as well as scalability.NoSQL as a paradigm shift.Hadoop can run and parallelise large scale batch computations on large amounts of data. however, there is a high latency in returning the results. It is not suitable for low latency.What are the features of a Big Data system?RobustFault TolerantHuman Fault TolerantData when you need itScaleableGeneralExtensibleReduced implementation complexityError handlingAuditing-- no different from a little Data Solution. Think inserts.
There are some things in life are so complicated and abstract that they’re awesome. Eternity, cosmic significance, and the infinite universe are just a few of these awesome, convoluted concepts that have kept us fascinated and confused since the beginning of human consciousness.Awe - perceptual expansion, such perceptual vastness that you literally have to configure your mental schemata just to accommodate, just to take in the scale, of the experienceanthological awakening, realization of the connectedness of all things, and also the continuum from inanimate to animate matter; all of it is nature, all of it is inevitable, all of it is emerging as part of the same evolutionary processPhysicist Freeman Dyson speaks of a new future where a new generation of artists will write genomes the way that Shakespeare used to write verses
Courtesy of WIPRO
Teradata and Lyn Langit slide.we’ve got 7 billion people, we got 6 billion devices90% of the world’s data was created in the last two years aloneNot the data that’s kept behind corporate walls. unstructured content, most of which didn’t even exist years ago: documents, tweets, images, videos posted to YouTube, data gathered from surveillance cameras. We post, we blog, we share, we tweet, we like or don’t like. We have a voice and we leave a digital trail. And every tweet we send is being followed, monitored, analyzed, acted on. Companies are analyzing social to find out what you’re thinking, to know what new products and services you want even before you do. A new initiative by the U.N. is actually using sentiment analyses to help predict the civil unrest, job losses, spending reductions, disease outbreaks
Digital Marketingoptimisation – golden path analysis, clickthroughtsDigital Exploration – Discovery, new marketsMachine generated analytics – logs, real time, telemetry. Location. Remote sensors.Data Retention – archivingTraditionally: Physics Experiments, Sensor data, Satellite data, …Now:Operational LogsCustomer behaviorSocial interactions online…From Terabytes in the 1990 over Petabytes today to Zetabytes in the future
What do we have now? It is like a vacuum tube; slow and expensive.Why did Big Data get big?
What do we have now? It is like a vacuum tube; slow and expensive.Why did Big Data get big?
Volume – data comes in one size – large.Variety – structured and unstructure data.Veracity – good and bad data.Velocity – fast moving.Value – business value
Unlike real crude oil, data can be re-used. It can be mined for profit.It needs to be re-shaped in order to be used.If you don’t’ have your data, you don’t have anything! You lose your business.
Thanks to @SiSense and Bruno Aziza
If you don’t’ have your data, you don’t have anything! You lose your business.
Relational databases are pushed to the limit.Data Management techniques haven't scaledTraditional systems haven't scaledBig data is about complexity as well as scalability.NoSQL as a paradigm shift.Hadoop can run and parallelise large scale batch computations on large amounts of data. however, there is a high latency in returning the results. It is not suitable for low latency.What are the features of a Big Data system?RobustFault TolerantHuman Fault TolerantData when you need itScaleableGeneralExtensibleReduced implementation complexityError handlingAuditing-- no different from a little Data Solution. Think inserts.
Big DataThis is a picture down the center isle of a shipping container from one of Microsoft’s datacenters. We put ~1800 computers inside one of these containers. Some of us had the privilege of working on the data storage and computational platform that powers Bing. We used 22 of these containers, spanning 40,000 machines where we stored over 100PB of data. This was three years ago, and now these servers are almost obsolete.Big Data is in constant motion and growing at an incredible rate,90% of the world’s data generated in just the past two years. That's remarkable growth. Technology history has taught us that the one with themost data wins. The empires of data like Twitter, Facebook, Yahoo all of whom are able to capitalize on the notion that data equates to power. More and more companies are increasingly utilizing Hadoop to power Big Data analytics and drive revenue and profit.It’s all about your Data.
Some examples of organizations that delivering new value based in the form of revenue growth, cost savings or creating entirely new business models.Yahoo - AS with Hive, Klout - AS with Hive (white paper), GE - Hive AnalyticsYahoo! (Gartner BI Excellence Award Winner) is driving growth for existing revenue streams:Yahoo! manages a powerful, scalable advertising exchange that includes publishers and advertisers.Advertisers want to get the most out of their investment by reaching their targeted audiences effectively and efficiently.Yahoo! needs visibility into how consumers are responding to ads alongmany dimensions (websites, creative, time of day, gender, age, location) to make the exchangework as efficiently and effectivelyas possible.Yahoo! doubled its revenue by allowing campaign managers to “tune” campaign targeting and creative.Yahoo! drove an increase in spending from advertisers since they got better performance by advertising through Yahoo!.Yahoo! TAO exposed customer segment performance to campaign managers and advertisers for the first time.Klout is creating new businesses and revenue streams:Klout’s mission is to help everyone understand and leverage their influence. Klout uses Big Data to unify the social web (consumers, brands, and partners) with social networking and activity, along with data to generate a Klout score and enable analysis, targeting, and social graphs.Helps consumers manage their “social brand.”Helps brands reach influencers at scale.Helps data partners enhance their services (customer loyalty, CRM, media and identity, and marketing). For example, the Palms uses Klout scores in addition to their normal customer rewards program to determine whether or not to upgrade their customers to a better room during their stay. The Huffington Post uses Klout to help serve the best curated Twitter content.Klout Case Study: http://www.microsoft.com/casestudies/Microsoft-SQL-Server-2012-Enterprise/Klout/Data-Services-Firm-Uses-Microsoft-BI-and-Hadoop-to-Boost-Insight-into-Big-Data/710000000129Case Study on Thailand’s Department of Special Investigations : http://www.microsoft.com/casestudies/Microsoft-SQL-Server-2012-Enterprise/Department-of-Special-Investigation/Thai-Law-Enforcement-Agency-Optimizes-Investigations-with-Big-Data-Solution/710000001175 GE is driving operational efficiencies:GE is running several use cases on its Hadoop cluster while incorporating several different disparate sources to produce results. Along with sentiment analysis, GE is running web analytics on its internal cloud structure and looking at load usage, user analytics, and failure mode analytics. GE built a recommendation engine for its intranet involving various press releases users might be interested in based on their function, user profiles, and prior visits to its site. GE is working with several types of remote monitoring and diagnostic data from energy and wind businesses.
Business Users need data. There is a paradigm shift towards it, despite what the cartoon says.
Processing Platform for Big Data ProcessingUsing the “Map-Reduce” Processing ParadigmWhen people talk about Hadoop they are often talking about specific computational patterns including map reduce, which emerged as a method to process lots of unstructured data on top of a distributed storage system in a highly fault tolerant and embarrassingly scalable way. Hadoop allows us to store and process large amounts of data on commodity hardware. In the past you would spend large amounts of money on very specialized hardware. Today you can do this with off the shelf hardware running Hadoop. Now, Hadoop doesn’t have a monopoly on “big”, “real time” or “unstructured” but does provide some unique capabilities.
Assuming that the volumes of data are larger than those conventional relational database infrastructures can cope with, processing options break down broadly into a choice between massively parallel processing architectures — data warehouses or databases such as Greenplum — and Apache Hadoop-based solutions. This choice is often informed by the degree to which the one of the other "Vs" — variety — comes into play. Typically, data warehousing approaches involve predetermined schemas, suiting a regular and slowly evolving dataset. Apache Hadoop, on the other hand, places no conditions on the structure of the data it can process.
Hadoop, on the other hand, places no conditions on the structure of the data it can process.
I see the real breakthrough insights coming through when you take what is the traditional "Business Intelligence" and add more capabilities like machine learning, predictive analysis, statistical analysis, large scale graph processing, pattern mining, trend analysis, economic modeling. All of which today are a reality in Hadoop. The implications of this are quite astounding when you think about it. This is huge.
Big Data; in terms of data volume, variability and velocity at scale are is the first problem. But the Big Data solutions and technology by themselves don't lead to solving business objectives. We don't have a Hadoop problem they have analytics, pattern mining, trend analysis, statistical inferenceing, economic modeling, market regression level problems.Data science starts where the utility class services like Big Data Hadoop end. The real opportunity is to expose data science to everyone.As powerful as Hadoop is, today it’s still more of a computer scientist’s or academically-trained analyst’s tool than it is an enterprise analytics product. Hadoop itself is controlled through programming code rather than anything that looks like it was designed for business unit personnel. Hadoop data is often more “raw” and “wild” than data typically fed to data warehouse and OLAP (Online Analytical Processing) systems. This is where I and Microsoft see opportunity. Essentially; wouldn't it be cool if mere mortals could use this stuff and consume insights that are directly coming from Hadoop? Microsoft HDInsight enables you to gain insight from virtually any data, connect with the world of data, improve decision making, and enhance the development of the next generation of products and services.Nearly everyone in your organization can analyze and make more informed decisions with the right tools.PowerPivot for Microsoft Excel and Power View for SharePoint give nearly all users a view into structured and unstructured data.With the Hive Add-in for Excel and Hive ODBC Driver, almost anyone in your organization can directly access Hadoop datafrom end-user tools.Hadoop simplifies programming for developers with JavaScript for MapReduce jobs. The JavaScriptimplementation can also reduce your code by up to 10 times compared to Java.
The second thing I want to talk about is Hadoop and how Hadoop is setup to deliver Breakthrough Insights from your data.How many of you are familiar with Hadoop? How many of you are using Hadoop for projects today?How many are planning on using Hadoop in the next 12mo? How about in the cloud?When people talk about Hadoop they are often talking about specific computational patterns including map reduce, which emerged as a method to process lots of unstructured data on top of a distributed storage system in a highly fault tolerant and embarrassingly scalable way. Hadoop allows us to store and process large amounts of data on commodity hardware. In the past you would spend large amounts of money on very specialized hardware. Today you can do this with off the shelf hardware running Hadoop. Now, Hadoop doesn’t have a monopoly on “big”, “real time” or “unstructured” but does provide some unique capabilities.
The second thing I want to talk about is Hadoop and how Hadoop is setup to deliver Breakthrough Insights from your data.How many of you are familiar with Hadoop? How many of you are using Hadoop for projects today?How many are planning on using Hadoop in the next 12mo? How about in the cloud?When people talk about Hadoop they are often talking about specific computational patterns including map reduce, which emerged as a method to process lots of unstructured data on top of a distributed storage system in a highly fault tolerant and embarrassingly scalable way. Hadoop allows us to store and process large amounts of data on commodity hardware. In the past you would spend large amounts of money on very specialized hardware. Today you can do this with off the shelf hardware running Hadoop. Now, Hadoop doesn’t have a monopoly on “big”, “real time” or “unstructured” but does provide some unique capabilities.
There are other talks that will go into Big Data and Hadoop so we’ll only do a quick overview of that right now. We’ll spend most of our time on Hive.